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May 25, 2026
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
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Study of Wurtzite Crystal Phase Stabilization in Heterostructured Ga(As,P) Nanowires

Semiconductors. 2020. Vol. 14.
Y. S. Berdnikov
In press

GaP as well as GaAs has face-centered cubic crystal phase at standard conditions. Despite it, GaP and GaAs nanowires frequently grow in a metastable hexagonal crystal phase called wurtzite. In this work, stable growth of nanowire in the metastable phase is explained by accounting the elastic strain in the nucleus of a new layer. Crystal phase switch in heterostructured Ga(As,P) nanowire caused by heterointerface was studied theoretically and experimentally. Length of wurtzite segment after the switch of crystal phase linearly increases with nanowire diameter. Such dependence can not be explained by only kinetically driven effects. We consider such dependence as an evidence of elastic strain stabilization of the metastable phase in nanowires.
 

Research target: Nanotechnologies Physics
Language: English
DOI
Keywords: gallium phosphidenanowirescrystal phasesgallium arsenidewurtzite
Publication based on the results of:
Optoelectronic devices for optical interconnects and optical on-chip systems based on semiconductor nanomaterials (2020)
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